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首页> 外文期刊>EURO journal of transportation and logistics >Identification and estimation of latent group-level-effects in infrastructure performance modeling
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Identification and estimation of latent group-level-effects in infrastructure performance modeling

机译：在基础设施性能建模中识别和估计潜在的群体水平效应

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As in other panel data analyses, the presence of unobserved heterogeneity is a critical issue in the estimation of infrastructure performance models. In the literature, this issue has been addressed by formulating variable intercept, fixed or random effects models under the assumptions that (1) heterogeneity stems from facility/individual-level effects, and that (2) the coefficients are constant and homogeneous across the population. In contrast, we present mixture regression as a performance modeling framework. The approach relies on the assumption that the underlying population is comprised of a finite set of classes/segments in unknown proportions. The segmentation basis is latent meaning that the criteria to establish the number and type of segments are related to unobserved heterogeneity manifested in facility performance/deterioration. The segments are characterized by a set of commonly specified regression equations, which allows for the identification and estimation of coefficients, i.e., group-level effects, that differ in terms of their level-of-significance, magnitude or sign. We also derive an instance of the Expectation-Maximization Algorithm to estimate the associated parameters, and to assign facilities to the population segments. To illustrate the framework, we analyze the performance of a panel of 131 pavements from the AASHO Road Test. The results suggest both observed and unobserved sources of heterogeneity in the panel. The heterogeneity is captured by differential group-level effects, which we estimate and interpret. We also discuss how these effects can be exploited in the development of resource allocation strategies. We also compare the mixture regression model to established benchmarks.
机译：与其他面板数据分析一样，未观察到的异质性的存在是基础架构性能模型估计中的关键问题。在文献中，这个问题已通过以下假设来解决：在以下条件下制定变量拦截，固定或随机效应模型：（1）异质性源于设施/个人水平的效应，（2）系数在总体中是恒定且均质的。相反，我们将混合回归作为性能建模框架。该方法基于以下假设：基础人口由一组未知比例的有限类别/细分组成。分段的基础是潜在的，这意味着确定分段数量和类型的标准与设施性能/恶化中表现出的未观察到的异质性有关。这些段由一组通常指定的回归方程式表征，该方程式可用于识别和估计系数（即组级别效应），这些系数的显着性级别，幅度或符号不同。我们还派生了Expectation-Maximization算法的一个实例，以估计相关的参数，并为人口群体分配设施。为了说明该框架，我们分析了AASHO道路测试中131个铺装路面的性能。结果表明小组中观察到的和未观察到的异质性来源。异质性是由不同的组级效应捕获的，我们对其进行了估计和解释。我们还将讨论如何在开发资源分配策略时利用这些影响。我们还将混合回归模型与已建立的基准进行比较。

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